Exam Professional Machine Learning Engineer topic 1 question 179 discussion

Use the Predictor interface to implement a custom prediction routine. This allows you to include the preprocessing and postprocessing steps in the same deployment package as your model. Build the custom container, which packages your model and the associated preprocessing and postprocessing code together, simplifying deployment. Upload the container to Vertex AI Model Registry. This makes your model available for deployment on Vertex AI. Deploy it to a Vertex AI endpoint. This allows your model to be used for online serving. https://blog.thecloudside.com/custom-predict-routines-in-vertex-ai-46a7473c95db

This approach minimizes code changes and infrastructure maintenance by leveraging Vertex AI's managed services for deployment. Implementing the preprocessing and postprocessing steps in a FastAPI server within a Docker container allows you to handle these steps at serving time efficiently. Deploying this Docker image to a Vertex AI endpoint simplifies the deployment process and reduces the burden of managing the infrastructure.

Option C is a good choice if You have specific requirements for preprocessing or postprocessing that can't be met by the prebuilt XGBoost serving container. You need more control over the deployment process or want to integrate with other services. You're comfortable building and managing custom containers. However, if you just want a simple, straightforward way to deploy your model as a RESTful API, Option D (using the XGBoost prebuilt serving container) might be a better fit!

FastAPI allows to create a lightweight HTTP server with minimal code.

し Pre-built XGBoost container already includes pre- and postprocessing steps.

My answer C: Considering pre- and postprocessing implementation, The option C directly deals with implementing the processing steps in a custom container, offering full control over their placement and execution. This documentation says: “Custom prediction routines (CPR) lets you build [custom containers](https://cloud.google.com/vertex-ai/docs/predictions/use-custom-container) with pre/post processing code easily, without dealing with the details of setting up an HTTP server or building a container from scratch.” https://cloud.google.com/vertex-ai/docs/predictions/custom-prediction-routines So, it is better to use C instead of A or B. D is better because it offers the option of pre and post-processing, which is not available in D due to its use of prebuilt serving.

C . Build the custom container, upload the container to Vertex AI Model Registry, and deploy it to a Vertex AI endpoint. This option allows you to leverage the power and simplicity of Vertex AI to serve your XGBoost model with minimal effort and customization. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained XGBoost model to an online prediction endpoint, which can provide low-latency predictions for individual instances. A custom prediction routine (CPR) is a Python script that defines the logic for preprocessing the input data, running the prediction, and postprocessing the output data.

Considering the goal of minimizing code changes, infrastructure maintenance, and quickly deploying the model into production, option D seems to be a pragmatic approach. It leverages the prebuilt XGBoost serving container in Vertex AI, providing a managed environment for serving. The pre- and postprocessing steps can be implemented in the Golang backend service, maintaining consistency with the existing Golang implementation and reducing the need for significant code changes.

I would say D